Many network assurance systems rely on predefined rules to determine the health of the network. In turn, these rules can be used to trigger corrective measures and/or notify a network administrator as to the health of the network. For instance, in an assurance system for a wireless network, one rule may comprise a defined threshold for what is considered as an acceptable number of clients per access point (AP) or the channel interference, itself. More complex rules may also be created to capture conditions over time, such as a number of events in a given time window or rates of variation of metrics (e.g., the client count, channel utilization, etc.).
In contrast to using predefined health status rules, a promising new area of interest with respect to network assurance systems is the use of machine learning to evaluate, predict, and diagnose the health status of a subject network. Notably, Deep Neural Networks (DNNs) often achieve impressive predictive accuracy, but they lack the explanatory power that is needed in industrial applications. This is particularly true when trying to use Recurrent Neural Networks (RNNs) to capture the dynamics of network elements (e.g., routers, switches, controllers, access points) and exploiting the hidden state information to distinguish different classes of behavior. In this case, the dimensions of the state space are most often uninterpretable without ad hoc mechanisms.